---
title: Leverage AI accelerators
description: Understand how AI accelerators work and how you can leverage them to get value from code-first machine learning workflows.

---

# Leverage AI accelerators {: #leverage-ai-accelerators }

After reviewing [how to get started with DataRobt as a code-first user](gs-code) and determining the platform in which you want to work, you can browse AI accelerators to learn about workflows you may want to explore via code.

## AI Accelerator overview

If you do not wish to begin coding from scratch, or want to further understand how to leverage DataRobot's capabilities from a code-centric perspective, browse DataRobot's many [AI accelerators](https://github.com/datarobot-community/ai-accelerators){ target=_blank } that outline common use cases and machine learning workflows using version 3.x of DataRobot's Python client.

If a particular use case is satisfactory to you, you can download it from DataRobot's AI accelerator repo and upload it to DataRobot Notebooks to test it , copy code to use as a template, and more to leverage your code-first experience.

The table below provides some sample accelerators for you to browse.

Topic | Describes... |
----- | ------ |
[Automated Feature Discovery with Multiple Tables](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced-experimentation/AFD){ target=_blank } | Use a repeatable end-to-end AFD workflow in Snowflake from data import to making batch predictions.
[Azure storage end-to-end workflow with DataRobot](https://github.com/datarobot-community/ai-accelerators/blob/main/end-to-end/Azure_End_to_End.ipynb){ target=_blank } | This workflow ingests a dataset hosted in an Azure blob container, trains a series of models using DataRobot's AutoML capabilities, deploys a recommended model, and sets up a batch prediction job that writes predictions back to the original container.
[Creating Custom Blueprints with Composable ML](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced-experimentation/custom_blueprints) | Learn how to create custom blueprints.
[Customize Lift Charts](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced-experimentation/customizing_lift_charts){ target=_blank } | Learn how to create a custom lift chart.
[Deploy a DataRobot model into AWS SageMaker](https://github.com/datarobot-community/ai-accelerators/tree/main/end-to-end/sagemaker_deployment){ target=_blank } | Build a model with DataRobot which will then be deployed and hosted within AWS SageMaker.
[End-to-end demand forecasting and retraining workflow](https://github.com/datarobot-community/ai-accelerators/blob/main/end-to-end/Demand_forecasting_retraining/End_to_end_demand_forecasting_retraining.ipynb){ target=_blank } | Set up retraining for time series and execute it when a model degrades. |
[End-to-end Time Series Demand Forecasting Workflow](https://github.com/datarobot-community/ai-accelerators/tree/main/end-to-end/End_to_end_demand_forecasting){ target=_blank } | Learn how to create a time series project with the API. |
[Feature Reduction with FIRE](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced-experimentation/feature_reduction_with_fire){ target=_blank } | Learn how to use FIRE for feature selection. |

To browse more AI accelerators, visit the [GitHub repo](https://github.com/datarobot-community/ai-accelerators){ target=_blank }.